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airlab-unibas / airlab

Image registration laboratory for 2D and 3D image data
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LCC and SSIM loss function error #28

Open wpstephen opened 4 years ago

wpstephen commented 4 years ago

I am trying to do some Rigid Registration on binary images of size 64x64 and I was playing around with different loss functions. But while trying to use both LCC and SSIM loss functions, I got following error: RuntimeError: Subtraction, the - operator, with a bool tensor is not supported. If you are trying to invert a mask, use the ~ or logical_not() operator instead.

I am not quite sure what this error entails, could anyone assist me here?

RobinSandkuehler commented 4 years ago

Hi,

I think this is versioning issue with PyTorch. We work on it in order to support the newest PyTorch version.

wpstephen commented 4 years ago

Can this be solved by downgrading to a lower version of PyTorch? If so, what version would work? Thank you!

On 23 Apr 2020, at 20:29, RobinSandkuehler notifications@github.com<mailto:notifications@github.com> wrote:

Hi,

I think this is versioning issue with PyTorch. We work on it in order to support the newest PyTorch version.

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RobinSandkuehler commented 4 years ago

Yes, Try Version 1.3 or below

wpstephen commented 4 years ago

Alright, I will try that. Thank you for your fast help. I will keep you up to date!

On 23 Apr 2020, at 20:32, RobinSandkuehler notifications@github.com<mailto:notifications@github.com> wrote:

Yes, Try Version 1.3 or below

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wpstephen commented 4 years ago

@RobinSandkuehler I tried on torch verson 1.3 and 1.2 but both produced another error:

RuntimeError: expected device cpu and dtype Byte but got device cpu and dtype Bool

I tried figuring out why dtype is set to Bool, but I cannot seem to figure out why it is happening

jlevy44 commented 4 years ago

@sumanthratna

mysakbm commented 2 years ago

The issue is that the mask is a boolean tensor. In the code on line 267, there is "mask = 1-mask" which is not supported by torch. You need to convert the mask into array of ints: mask = 1 - mask.type(th.uint8)